Overview

Dataset statistics

Number of variables24
Number of observations3325
Missing cells962
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory532.6 KiB
Average record size in memory164.0 B

Variable types

Categorical4
Text3
Numeric13
Boolean4

Alerts

compliancestatus has constant value ""Constant
councildistrictcode is highly overall correlated with latitude and 1 other fieldsHigh correlation
propertygfatotal is highly overall correlated with propertygfabuilding_s and 2 other fieldsHigh correlation
propertygfabuilding_s is highly overall correlated with propertygfatotal and 2 other fieldsHigh correlation
largestpropertyusetypegfa is highly overall correlated with propertygfatotal and 2 other fieldsHigh correlation
energystarscore is highly overall correlated with electricityHigh correlation
totalghgemissions is highly overall correlated with propertygfatotal and 2 other fieldsHigh correlation
latitude is highly overall correlated with councildistrictcode and 1 other fieldsHigh correlation
buildingtype is highly overall correlated with primarypropertytype and 1 other fieldsHigh correlation
primarypropertytype is highly overall correlated with buildingtype and 1 other fieldsHigh correlation
neighborhood is highly overall correlated with councildistrictcode and 1 other fieldsHigh correlation
electricity is highly overall correlated with energystarscoreHigh correlation
defaultdata is highly overall correlated with buildingtype and 1 other fieldsHigh correlation
steamuse is highly imbalanced (76.3%)Imbalance
electricity is highly imbalanced (98.4%)Imbalance
defaultdata is highly imbalanced (79.0%)Imbalance
energystarscore has 819 (24.6%) missing valuesMissing
compliancestatus has 121 (3.6%) missing valuesMissing
numberofbuildings is highly skewed (γ1 = 43.65087907)Skewed
propertygfatotal is highly skewed (γ1 = 24.30204906)Skewed
propertygfabuilding_s is highly skewed (γ1 = 27.86973423)Skewed
largestpropertyusetypegfa is highly skewed (γ1 = 30.23012777)Skewed
propertygfaparking has 2827 (85.0%) zerosZeros

Reproduction

Analysis started2023-07-10 19:06:17.999661
Analysis finished2023-07-10 19:07:00.264436
Duration42.26 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

buildingtype
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size26.1 KiB
NonResidential
1439 
Multifamily LR (1-4)
996 
Multifamily MR (5-9)
578 
Multifamily HR (10+)
 
109
SPS-District K-12
 
94
Other values (3)
 
109

Length

Max length20
Median length20
Mean length17.166015
Min length6

Characters and Unicode

Total characters57077
Distinct characters40
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNonResidential
2nd rowNonResidential
3rd rowNonResidential
4th rowNonResidential
5th rowNonResidential

Common Values

ValueCountFrequency (%)
NonResidential 1439
43.3%
Multifamily LR (1-4) 996
30.0%
Multifamily MR (5-9) 578
17.4%
Multifamily HR (10+) 109
 
3.3%
SPS-District K-12 94
 
2.8%
Nonresidential COS 84
 
2.5%
Campus 24
 
0.7%
Nonresidential WA 1
 
< 0.1%

Length

2023-07-10T21:07:00.448351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T21:07:00.857710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
multifamily 1683
24.5%
nonresidential 1524
22.2%
lr 996
14.5%
1-4 996
14.5%
mr 578
 
8.4%
5-9 578
 
8.4%
hr 109
 
1.6%
10 109
 
1.6%
sps-district 94
 
1.4%
k-12 94
 
1.4%
Other values (3) 109
 
1.6%

Most occurring characters

ValueCountFrequency (%)
i 6602
 
11.6%
l 4890
 
8.6%
3545
 
6.2%
t 3395
 
5.9%
a 3231
 
5.7%
R 3122
 
5.5%
n 3048
 
5.3%
e 3048
 
5.3%
M 2261
 
4.0%
- 1762
 
3.1%
Other values (30) 22173
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35981
63.0%
Uppercase Letter 8760
 
15.3%
Decimal Number 3554
 
6.2%
Space Separator 3545
 
6.2%
Dash Punctuation 1762
 
3.1%
Open Punctuation 1683
 
2.9%
Close Punctuation 1683
 
2.9%
Math Symbol 109
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6602
18.3%
l 4890
13.6%
t 3395
9.4%
a 3231
9.0%
n 3048
8.5%
e 3048
8.5%
u 1707
 
4.7%
m 1707
 
4.7%
f 1683
 
4.7%
y 1683
 
4.7%
Other values (6) 4987
13.9%
Uppercase Letter
ValueCountFrequency (%)
R 3122
35.6%
M 2261
25.8%
N 1524
17.4%
L 996
 
11.4%
S 272
 
3.1%
H 109
 
1.2%
C 108
 
1.2%
K 94
 
1.1%
P 94
 
1.1%
D 94
 
1.1%
Other values (3) 86
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1199
33.7%
4 996
28.0%
5 578
16.3%
9 578
16.3%
0 109
 
3.1%
2 94
 
2.6%
Space Separator
ValueCountFrequency (%)
3545
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1762
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1683
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1683
100.0%
Math Symbol
ValueCountFrequency (%)
+ 109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44741
78.4%
Common 12336
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6602
14.8%
l 4890
10.9%
t 3395
 
7.6%
a 3231
 
7.2%
R 3122
 
7.0%
n 3048
 
6.8%
e 3048
 
6.8%
M 2261
 
5.1%
u 1707
 
3.8%
m 1707
 
3.8%
Other values (19) 11730
26.2%
Common
ValueCountFrequency (%)
3545
28.7%
- 1762
14.3%
( 1683
13.6%
) 1683
13.6%
1 1199
 
9.7%
4 996
 
8.1%
5 578
 
4.7%
9 578
 
4.7%
0 109
 
0.9%
+ 109
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57077
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6602
 
11.6%
l 4890
 
8.6%
3545
 
6.2%
t 3395
 
5.9%
a 3231
 
5.7%
R 3122
 
5.5%
n 3048
 
5.3%
e 3048
 
5.3%
M 2261
 
4.0%
- 1762
 
3.1%
Other values (30) 22173
38.8%

primarypropertytype
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size26.1 KiB
Low-Rise Multifamily
966 
Mid-Rise Multifamily
561 
Small- and Mid-Sized Office
288 
Other
250 
Warehouse
187 
Other values (19)
1073 

Length

Max length27
Median length22
Mean length17.193383
Min length5

Characters and Unicode

Total characters57168
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel

Common Values

ValueCountFrequency (%)
Low-Rise Multifamily 966
29.1%
Mid-Rise Multifamily 561
16.9%
Small- and Mid-Sized Office 288
 
8.7%
Other 250
 
7.5%
Warehouse 187
 
5.6%
Large Office 170
 
5.1%
K-12 School 134
 
4.0%
Mixed Use Property 132
 
4.0%
High-Rise Multifamily 104
 
3.1%
Retail Store 89
 
2.7%
Other values (14) 444
13.4%

Length

2023-07-10T21:07:01.207992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
multifamily 1631
23.6%
low-rise 966
14.0%
mid-rise 561
 
8.1%
office 500
 
7.2%
small 288
 
4.2%
and 288
 
4.2%
mid-sized 288
 
4.2%
other 250
 
3.6%
warehouse 199
 
2.9%
large 170
 
2.5%
Other values (28) 1767
25.6%

Most occurring characters

ValueCountFrequency (%)
i 7501
 
13.1%
e 4476
 
7.8%
l 4357
 
7.6%
3583
 
6.3%
a 3002
 
5.3%
t 2755
 
4.8%
f 2671
 
4.7%
M 2651
 
4.6%
- 2369
 
4.1%
s 2154
 
3.8%
Other values (33) 21649
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42381
74.1%
Uppercase Letter 8528
 
14.9%
Space Separator 3583
 
6.3%
Dash Punctuation 2369
 
4.1%
Decimal Number 268
 
0.5%
Other Punctuation 39
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7501
17.7%
e 4476
10.6%
l 4357
10.3%
a 3002
 
7.1%
t 2755
 
6.5%
f 2671
 
6.3%
s 2154
 
5.1%
o 2078
 
4.9%
m 2048
 
4.8%
u 1979
 
4.7%
Other values (14) 9360
22.1%
Uppercase Letter
ValueCountFrequency (%)
M 2651
31.1%
R 1767
20.7%
L 1146
13.4%
S 978
 
11.5%
O 750
 
8.8%
W 268
 
3.1%
H 213
 
2.5%
U 157
 
1.8%
C 143
 
1.7%
K 134
 
1.6%
Other values (4) 321
 
3.8%
Decimal Number
ValueCountFrequency (%)
1 134
50.0%
2 134
50.0%
Space Separator
ValueCountFrequency (%)
3583
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2369
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50909
89.1%
Common 6259
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7501
14.7%
e 4476
 
8.8%
l 4357
 
8.6%
a 3002
 
5.9%
t 2755
 
5.4%
f 2671
 
5.2%
M 2651
 
5.2%
s 2154
 
4.2%
o 2078
 
4.1%
m 2048
 
4.0%
Other values (28) 17216
33.8%
Common
ValueCountFrequency (%)
3583
57.2%
- 2369
37.8%
1 134
 
2.1%
2 134
 
2.1%
/ 39
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7501
 
13.1%
e 4476
 
7.8%
l 4357
 
7.6%
3583
 
6.3%
a 3002
 
5.3%
t 2755
 
4.8%
f 2671
 
4.7%
M 2651
 
4.6%
- 2369
 
4.1%
s 2154
 
3.8%
Other values (33) 21649
37.9%
Distinct3219
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:01.593280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length10
Mean length10.005113
Min length9

Characters and Unicode

Total characters33267
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3143 ?
Unique (%)94.5%

Sample

1st row0659000030
2nd row0659000220
3rd row0659000475
4th row0659000640
5th row0659000970
ValueCountFrequency (%)
1625049001 8
 
0.2%
0925049346 5
 
0.2%
0002400002 5
 
0.2%
3224049012 5
 
0.2%
7666203240 4
 
0.1%
3624039009 4
 
0.1%
0225049077 3
 
0.1%
7954000005 3
 
0.1%
5036300605 3
 
0.1%
8809700040 3
 
0.1%
Other values (3210) 3284
98.7%
2023-07-10T21:07:02.243551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11170
33.6%
2 3118
 
9.4%
5 2903
 
8.7%
6 2674
 
8.0%
1 2654
 
8.0%
9 2353
 
7.1%
7 2333
 
7.0%
4 2134
 
6.4%
3 2040
 
6.1%
8 1881
 
5.7%
Other values (5) 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33260
> 99.9%
Lowercase Letter 3
 
< 0.1%
Space Separator 2
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11170
33.6%
2 3118
 
9.4%
5 2903
 
8.7%
6 2674
 
8.0%
1 2654
 
8.0%
9 2353
 
7.1%
7 2333
 
7.0%
4 2134
 
6.4%
3 2040
 
6.1%
8 1881
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
a 1
33.3%
n 1
33.3%
d 1
33.3%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33264
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11170
33.6%
2 3118
 
9.4%
5 2903
 
8.7%
6 2674
 
8.0%
1 2654
 
8.0%
9 2353
 
7.1%
7 2333
 
7.0%
4 2134
 
6.4%
3 2040
 
6.1%
8 1881
 
5.7%
Other values (2) 4
 
< 0.1%
Latin
ValueCountFrequency (%)
a 1
33.3%
n 1
33.3%
d 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11170
33.6%
2 3118
 
9.4%
5 2903
 
8.7%
6 2674
 
8.0%
1 2654
 
8.0%
9 2353
 
7.1%
7 2333
 
7.0%
4 2134
 
6.4%
3 2040
 
6.1%
8 1881
 
5.7%
Other values (5) 7
 
< 0.1%

councildistrictcode
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4439098
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:02.602886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1189641
Coefficient of variation (CV)0.47682428
Kurtosis-1.4456471
Mean4.4439098
Median Absolute Deviation (MAD)2
Skewness-0.072158098
Sum14776
Variance4.4900088
MonotonicityNot monotonic
2023-07-10T21:07:02.900337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 1022
30.7%
3 587
17.7%
2 503
15.1%
4 360
 
10.8%
5 333
 
10.0%
1 274
 
8.2%
6 246
 
7.4%
ValueCountFrequency (%)
1 274
 
8.2%
2 503
15.1%
3 587
17.7%
4 360
 
10.8%
5 333
 
10.0%
6 246
 
7.4%
7 1022
30.7%
ValueCountFrequency (%)
7 1022
30.7%
6 246
 
7.4%
5 333
 
10.0%
4 360
 
10.8%
3 587
17.7%
2 503
15.1%
1 274
 
8.2%

neighborhood
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size26.1 KiB
DOWNTOWN
562 
EAST
448 
MAGNOLIA / QUEEN ANNE
417 
GREATER DUWAMISH
371 
NORTHEAST
274 
Other values (8)
1253 

Length

Max length21
Median length10
Mean length10.114586
Min length4

Characters and Unicode

Total characters33631
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDOWNTOWN
2nd rowDOWNTOWN
3rd rowDOWNTOWN
4th rowDOWNTOWN
5th rowDOWNTOWN

Common Values

ValueCountFrequency (%)
DOWNTOWN 562
16.9%
EAST 448
13.5%
MAGNOLIA / QUEEN ANNE 417
12.5%
GREATER DUWAMISH 371
11.2%
NORTHEAST 274
8.2%
LAKE UNION 250
7.5%
NORTHWEST 219
 
6.6%
NORTH 183
 
5.5%
SOUTHWEST 158
 
4.8%
BALLARD 133
 
4.0%
Other values (3) 310
9.3%

Length

2023-07-10T21:07:03.271325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
downtown 562
10.8%
east 448
 
8.6%
magnolia 417
 
8.0%
417
 
8.0%
queen 417
 
8.0%
anne 417
 
8.0%
greater 371
 
7.1%
duwamish 371
 
7.1%
northeast 274
 
5.3%
union 250
 
4.8%
Other values (8) 1253
24.1%

Most occurring characters

ValueCountFrequency (%)
N 4098
12.2%
E 3737
11.1%
A 3456
10.3%
T 3186
9.5%
O 2720
 
8.1%
1872
 
5.6%
W 1872
 
5.6%
S 1818
 
5.4%
R 1766
 
5.3%
H 1300
 
3.9%
Other values (11) 7806
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 31342
93.2%
Space Separator 1872
 
5.6%
Other Punctuation 417
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 4098
13.1%
E 3737
11.9%
A 3456
11.0%
T 3186
10.2%
O 2720
8.7%
W 1872
 
6.0%
S 1818
 
5.8%
R 1766
 
5.6%
H 1300
 
4.1%
U 1291
 
4.1%
Other values (9) 6098
19.5%
Space Separator
ValueCountFrequency (%)
1872
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31342
93.2%
Common 2289
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4098
13.1%
E 3737
11.9%
A 3456
11.0%
T 3186
10.2%
O 2720
8.7%
W 1872
 
6.0%
S 1818
 
5.8%
R 1766
 
5.6%
H 1300
 
4.1%
U 1291
 
4.1%
Other values (9) 6098
19.5%
Common
ValueCountFrequency (%)
1872
81.8%
/ 417
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 4098
12.2%
E 3737
11.1%
A 3456
10.3%
T 3186
9.5%
O 2720
 
8.1%
1872
 
5.6%
W 1872
 
5.6%
S 1818
 
5.4%
R 1766
 
5.3%
H 1300
 
3.9%
Other values (11) 7806
23.2%

numberofbuildings
Real number (ℝ)

SKEWED 

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1344361
Minimum1
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:03.611166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum111
Range110
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1127163
Coefficient of variation (CV)1.8623493
Kurtosis2213.412
Mean1.1344361
Median Absolute Deviation (MAD)0
Skewness43.650879
Sum3772
Variance4.4635701
MonotonicityNot monotonic
2023-07-10T21:07:03.934436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 3226
97.0%
2 36
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 9
 
0.3%
6 5
 
0.2%
8 3
 
0.1%
14 2
 
0.1%
9 2
 
0.1%
10 2
 
0.1%
Other values (6) 6
 
0.2%
ValueCountFrequency (%)
1 3226
97.0%
2 36
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 9
 
0.3%
6 5
 
0.2%
7 1
 
< 0.1%
8 3
 
0.1%
9 2
 
0.1%
10 2
 
0.1%
ValueCountFrequency (%)
111 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%
16 1
 
< 0.1%
14 2
0.1%
11 1
 
< 0.1%
10 2
0.1%
9 2
0.1%
8 3
0.1%
7 1
 
< 0.1%

numberoffloors
Real number (ℝ)

Distinct49
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7287218
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:04.302148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile12
Maximum99
Range98
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.516339
Coefficient of variation (CV)1.1665603
Kurtosis55.781252
Mean4.7287218
Median Absolute Deviation (MAD)2
Skewness5.9209841
Sum15723
Variance30.429996
MonotonicityNot monotonic
2023-07-10T21:07:04.819773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3 680
20.5%
4 676
20.3%
1 474
14.3%
2 432
13.0%
6 303
9.1%
5 294
8.8%
7 146
 
4.4%
8 63
 
1.9%
10 32
 
1.0%
11 32
 
1.0%
Other values (39) 193
 
5.8%
ValueCountFrequency (%)
1 474
14.3%
2 432
13.0%
3 680
20.5%
4 676
20.3%
5 294
8.8%
6 303
9.1%
7 146
 
4.4%
8 63
 
1.9%
9 18
 
0.5%
10 32
 
1.0%
ValueCountFrequency (%)
99 1
 
< 0.1%
76 1
 
< 0.1%
63 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
49 1
 
< 0.1%
47 1
 
< 0.1%
46 1
 
< 0.1%
42 6
0.2%
41 3
0.1%

propertygfatotal
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3152
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95117.731
Minimum11285
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:05.206377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11285
5-th percentile21300.6
Q128492
median44432
Q391560
95-th percentile320721.4
Maximum9320156
Range9308871
Interquartile range (IQR)63068

Descriptive statistics

Standard deviation219227.93
Coefficient of variation (CV)2.3048061
Kurtosis953.47919
Mean95117.731
Median Absolute Deviation (MAD)20017
Skewness24.302049
Sum3.1626646 × 108
Variance4.8060884 × 1010
MonotonicityNot monotonic
2023-07-10T21:07:05.594292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
21600 7
 
0.2%
28800 7
 
0.2%
24000 6
 
0.2%
30240 4
 
0.1%
22320 4
 
0.1%
30720 4
 
0.1%
43380 3
 
0.1%
21200 3
 
0.1%
Other values (3142) 3270
98.3%
ValueCountFrequency (%)
11285 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
13157 1
< 0.1%
13661 1
< 0.1%
14101 1
< 0.1%
15398 1
< 0.1%
16000 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
2200000 1
< 0.1%
1952220 1
< 0.1%
1765970 1
< 0.1%
1605578 1
< 0.1%
1592914 1
< 0.1%
1585960 1
< 0.1%
1536606 1
< 0.1%
1400000 1
< 0.1%
1380959 1
< 0.1%

propertygfaparking
Real number (ℝ)

ZEROS 

Distinct490
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8092.6767
Minimum0
Maximum512608
Zeros2827
Zeros (%)85.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:05.961080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile48074.4
Maximum512608
Range512608
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32553.075
Coefficient of variation (CV)4.022535
Kurtosis58.120796
Mean8092.6767
Median Absolute Deviation (MAD)0
Skewness6.6047898
Sum26908150
Variance1.0597027 × 109
MonotonicityNot monotonic
2023-07-10T21:07:06.344995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2827
85.0%
13320 3
 
0.1%
12960 2
 
0.1%
10800 2
 
0.1%
100176 2
 
0.1%
22000 2
 
0.1%
30000 2
 
0.1%
25800 2
 
0.1%
20416 2
 
0.1%
3029 1
 
< 0.1%
Other values (480) 480
 
14.4%
ValueCountFrequency (%)
0 2827
85.0%
38 1
 
< 0.1%
260 1
 
< 0.1%
415 1
 
< 0.1%
604 1
 
< 0.1%
756 1
 
< 0.1%
800 1
 
< 0.1%
919 1
 
< 0.1%
1263 1
 
< 0.1%
1392 1
 
< 0.1%
ValueCountFrequency (%)
512608 1
< 0.1%
407795 1
< 0.1%
389860 1
< 0.1%
368980 1
< 0.1%
335109 1
< 0.1%
327680 1
< 0.1%
319400 1
< 0.1%
303707 1
< 0.1%
285688 1
< 0.1%
285000 1
< 0.1%

propertygfabuilding_s
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3148
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87025.055
Minimum3636
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:06.677178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile21036.8
Q127770
median43330
Q384739
95-th percentile283310.6
Maximum9320156
Range9316520
Interquartile range (IQR)56969

Descriptive statistics

Standard deviation208189.04
Coefficient of variation (CV)2.3922886
Kurtosis1173.0254
Mean87025.055
Median Absolute Deviation (MAD)19050
Skewness27.869734
Sum2.8935831 × 108
Variance4.3342678 × 1010
MonotonicityNot monotonic
2023-07-10T21:07:07.047683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
28800 7
 
0.2%
21600 7
 
0.2%
24000 6
 
0.2%
30240 4
 
0.1%
30720 4
 
0.1%
22320 4
 
0.1%
22344 3
 
0.1%
25200 3
 
0.1%
Other values (3138) 3270
98.3%
ValueCountFrequency (%)
3636 1
< 0.1%
10925 1
< 0.1%
11285 1
< 0.1%
11440 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
12806 1
< 0.1%
13157 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
2200000 1
< 0.1%
1765970 1
< 0.1%
1632820 1
< 0.1%
1592914 1
< 0.1%
1380959 1
< 0.1%
1323055 1
< 0.1%
1258280 1
< 0.1%
1215718 1
< 0.1%
1195387 1
< 0.1%
Distinct460
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:07.519551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length255
Median length162
Mean length25.96
Min length5

Characters and Unicode

Total characters86317
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)9.3%

Sample

1st rowHotel
2nd rowHotel, Parking, Restaurant
3rd rowHotel
4th rowHotel
5th rowHotel, Parking, Swimming Pool
ValueCountFrequency (%)
multifamily 1688
17.3%
housing 1688
17.3%
parking 1078
11.0%
office 951
 
9.7%
store 465
 
4.8%
other 413
 
4.2%
retail 398
 
4.1%
warehouse 277
 
2.8%
non-refrigerated 260
 
2.7%
179
 
1.8%
Other values (96) 2385
24.4%
2023-07-10T21:07:08.234525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 9267
 
10.7%
6457
 
7.5%
e 5623
 
6.5%
a 5191
 
6.0%
l 4895
 
5.7%
t 4883
 
5.7%
u 4214
 
4.9%
r 4202
 
4.9%
n 4130
 
4.8%
o 3961
 
4.6%
Other values (42) 33494
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65618
76.0%
Uppercase Letter 10235
 
11.9%
Space Separator 6457
 
7.5%
Other Punctuation 3024
 
3.5%
Dash Punctuation 625
 
0.7%
Decimal Number 284
 
0.3%
Open Punctuation 37
 
< 0.1%
Close Punctuation 37
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9267
14.1%
e 5623
 
8.6%
a 5191
 
7.9%
l 4895
 
7.5%
t 4883
 
7.4%
u 4214
 
6.4%
r 4202
 
6.4%
n 4130
 
6.3%
o 3961
 
6.0%
f 3933
 
6.0%
Other values (12) 15319
23.3%
Uppercase Letter
ValueCountFrequency (%)
H 1887
18.4%
M 1837
17.9%
O 1386
13.5%
P 1225
12.0%
S 1015
9.9%
R 953
9.3%
W 353
 
3.4%
C 337
 
3.3%
N 266
 
2.6%
F 221
 
2.2%
Other values (11) 755
7.4%
Other Punctuation
ValueCountFrequency (%)
, 2653
87.7%
/ 359
 
11.9%
& 12
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 142
50.0%
1 142
50.0%
Space Separator
ValueCountFrequency (%)
6457
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 625
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75853
87.9%
Common 10464
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9267
 
12.2%
e 5623
 
7.4%
a 5191
 
6.8%
l 4895
 
6.5%
t 4883
 
6.4%
u 4214
 
5.6%
r 4202
 
5.5%
n 4130
 
5.4%
o 3961
 
5.2%
f 3933
 
5.2%
Other values (33) 25554
33.7%
Common
ValueCountFrequency (%)
6457
61.7%
, 2653
25.4%
- 625
 
6.0%
/ 359
 
3.4%
2 142
 
1.4%
1 142
 
1.4%
( 37
 
0.4%
) 37
 
0.4%
& 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9267
 
10.7%
6457
 
7.5%
e 5623
 
6.5%
a 5191
 
6.0%
l 4895
 
5.7%
t 4883
 
5.7%
u 4214
 
4.9%
r 4202
 
4.9%
n 4130
 
4.8%
o 3961
 
4.6%
Other values (42) 33494
38.8%
Distinct55
Distinct (%)1.7%
Missing11
Missing (%)0.3%
Memory size26.1 KiB
2023-07-10T21:07:08.564652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length52
Median length19
Mean length16.287266
Min length5

Characters and Unicode

Total characters53976
Distinct characters51
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.2%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel
ValueCountFrequency (%)
multifamily 1649
27.1%
housing 1649
27.1%
office 536
 
8.8%
warehouse 211
 
3.5%
non-refrigerated 199
 
3.3%
other 174
 
2.9%
store 137
 
2.3%
k-12 134
 
2.2%
school 134
 
2.2%
facility 98
 
1.6%
Other values (78) 1158
19.0%
2023-07-10T21:07:09.327196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6754
 
12.5%
l 4084
 
7.6%
u 3756
 
7.0%
t 3060
 
5.7%
o 3033
 
5.6%
e 3007
 
5.6%
f 2973
 
5.5%
a 2776
 
5.1%
2765
 
5.1%
n 2368
 
4.4%
Other values (41) 19400
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43897
81.3%
Uppercase Letter 6378
 
11.8%
Space Separator 2765
 
5.1%
Dash Punctuation 439
 
0.8%
Decimal Number 268
 
0.5%
Other Punctuation 195
 
0.4%
Open Punctuation 17
 
< 0.1%
Close Punctuation 17
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6754
15.4%
l 4084
9.3%
u 3756
 
8.6%
t 3060
 
7.0%
o 3033
 
6.9%
e 3007
 
6.9%
f 2973
 
6.8%
a 2776
 
6.3%
n 2368
 
5.4%
s 2170
 
4.9%
Other values (11) 9916
22.6%
Uppercase Letter
ValueCountFrequency (%)
H 1779
27.9%
M 1734
27.2%
O 722
11.3%
S 467
 
7.3%
R 390
 
6.1%
W 281
 
4.4%
N 199
 
3.1%
C 198
 
3.1%
K 134
 
2.1%
F 109
 
1.7%
Other values (11) 365
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/ 165
84.6%
, 20
 
10.3%
& 10
 
5.1%
Decimal Number
ValueCountFrequency (%)
2 134
50.0%
1 134
50.0%
Space Separator
ValueCountFrequency (%)
2765
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 439
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50275
93.1%
Common 3701
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6754
13.4%
l 4084
 
8.1%
u 3756
 
7.5%
t 3060
 
6.1%
o 3033
 
6.0%
e 3007
 
6.0%
f 2973
 
5.9%
a 2776
 
5.5%
n 2368
 
4.7%
s 2170
 
4.3%
Other values (32) 16294
32.4%
Common
ValueCountFrequency (%)
2765
74.7%
- 439
 
11.9%
/ 165
 
4.5%
2 134
 
3.6%
1 134
 
3.6%
, 20
 
0.5%
( 17
 
0.5%
) 17
 
0.5%
& 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6754
 
12.5%
l 4084
 
7.6%
u 3756
 
7.0%
t 3060
 
5.7%
o 3033
 
5.6%
e 3007
 
5.6%
f 2973
 
5.5%
a 2776
 
5.1%
2765
 
5.1%
n 2368
 
4.4%
Other values (41) 19400
35.9%

largestpropertyusetypegfa
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3087
Distinct (%)93.2%
Missing11
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean79274.199
Minimum5656
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:09.712584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5656
5-th percentile17521.8
Q125138.75
median39956
Q377038.25
95-th percentile244684.8
Maximum9320156
Range9314500
Interquartile range (IQR)51899.5

Descriptive statistics

Standard deviation202169.86
Coefficient of variation (CV)2.5502605
Kurtosis1324.8584
Mean79274.199
Median Absolute Deviation (MAD)17627.5
Skewness30.230128
Sum2.627147 × 108
Variance4.0872653 × 1010
MonotonicityNot monotonic
2023-07-10T21:07:10.097155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24000 9
 
0.3%
22000 8
 
0.2%
30000 8
 
0.2%
20000 7
 
0.2%
21600 7
 
0.2%
45000 5
 
0.2%
15000 5
 
0.2%
24288 5
 
0.2%
36000 5
 
0.2%
28800 5
 
0.2%
Other values (3077) 3250
97.7%
(Missing) 11
 
0.3%
ValueCountFrequency (%)
5656 1
< 0.1%
6455 1
< 0.1%
6601 1
< 0.1%
6900 1
< 0.1%
7245 1
< 0.1%
7387 1
< 0.1%
7501 1
< 0.1%
7583 1
< 0.1%
7758 1
< 0.1%
8061 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
1719643 1
< 0.1%
1680937 1
< 0.1%
1639334 1
< 0.1%
1585960 1
< 0.1%
1350182 1
< 0.1%
1314475 1
< 0.1%
1191115 1
< 0.1%
1172127 1
< 0.1%
1011135 1
< 0.1%

energystarscore
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)4.0%
Missing819
Missing (%)24.6%
Infinite0
Infinite (%)0.0%
Mean67.821229
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:10.358799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q153
median75
Q390
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.703263
Coefficient of variation (CV)0.39373015
Kurtosis-0.21900622
Mean67.821229
Median Absolute Deviation (MAD)17
Skewness-0.85720143
Sum169960
Variance713.06424
MonotonicityNot monotonic
2023-07-10T21:07:10.753013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 91
 
2.7%
98 72
 
2.2%
96 64
 
1.9%
89 58
 
1.7%
93 57
 
1.7%
92 53
 
1.6%
95 51
 
1.5%
94 49
 
1.5%
91 49
 
1.5%
97 48
 
1.4%
Other values (90) 1914
57.6%
(Missing) 819
24.6%
ValueCountFrequency (%)
1 33
1.0%
2 10
 
0.3%
3 13
 
0.4%
4 5
 
0.2%
5 8
 
0.2%
6 8
 
0.2%
7 10
 
0.3%
8 10
 
0.3%
9 5
 
0.2%
10 10
 
0.3%
ValueCountFrequency (%)
100 91
2.7%
99 48
1.4%
98 72
2.2%
97 48
1.4%
96 64
1.9%
95 51
1.5%
94 49
1.5%
93 57
1.7%
92 53
1.6%
91 49
1.5%

steamuse
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
3196 
True
 
129
ValueCountFrequency (%)
False 3196
96.1%
True 129
 
3.9%
2023-07-10T21:07:11.092322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

electricity
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
True
3320 
False
 
5
ValueCountFrequency (%)
True 3320
99.8%
False 5
 
0.2%
2023-07-10T21:07:11.404562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

naturalgas
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
True
2094 
False
1231 
ValueCountFrequency (%)
True 2094
63.0%
False 1231
37.0%
2023-07-10T21:07:11.719058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

defaultdata
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
3215 
True
 
110
ValueCountFrequency (%)
False 3215
96.7%
True 110
 
3.3%
2023-07-10T21:07:12.035993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

compliancestatus
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing121
Missing (%)3.6%
Memory size26.1 KiB
Compliant
3204 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters28836
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompliant
2nd rowCompliant
3rd rowCompliant
4th rowCompliant
5th rowCompliant

Common Values

ValueCountFrequency (%)
Compliant 3204
96.4%
(Missing) 121
 
3.6%

Length

2023-07-10T21:07:12.346235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T21:07:12.695711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
compliant 3204
100.0%

Most occurring characters

ValueCountFrequency (%)
C 3204
11.1%
o 3204
11.1%
m 3204
11.1%
p 3204
11.1%
l 3204
11.1%
i 3204
11.1%
a 3204
11.1%
n 3204
11.1%
t 3204
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25632
88.9%
Uppercase Letter 3204
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3204
12.5%
m 3204
12.5%
p 3204
12.5%
l 3204
12.5%
i 3204
12.5%
a 3204
12.5%
n 3204
12.5%
t 3204
12.5%
Uppercase Letter
ValueCountFrequency (%)
C 3204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28836
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 3204
11.1%
o 3204
11.1%
m 3204
11.1%
p 3204
11.1%
l 3204
11.1%
i 3204
11.1%
a 3204
11.1%
n 3204
11.1%
t 3204
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 3204
11.1%
o 3204
11.1%
m 3204
11.1%
p 3204
11.1%
l 3204
11.1%
i 3204
11.1%
a 3204
11.1%
n 3204
11.1%
t 3204
11.1%

totalghgemissions
Real number (ℝ)

HIGH CORRELATION 

Distinct2789
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.47031
Minimum0.4
Maximum16870.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:13.129663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile3.94
Q19.72
median34.22
Q394.23
95-th percentile392.888
Maximum16870.98
Range16870.58
Interquartile range (IQR)84.51

Descriptive statistics

Standard deviation541.93744
Coefficient of variation (CV)4.4985144
Kurtosis469.83732
Mean120.47031
Median Absolute Deviation (MAD)28.06
Skewness19.386458
Sum400563.79
Variance293696.19
MonotonicityNot monotonic
2023-07-10T21:07:13.520357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.95 7
 
0.2%
4.2 6
 
0.2%
4.43 5
 
0.2%
4.76 5
 
0.2%
9.29 5
 
0.2%
5.07 5
 
0.2%
3.63 5
 
0.2%
5.46 5
 
0.2%
4.52 5
 
0.2%
4.8 5
 
0.2%
Other values (2779) 3272
98.4%
ValueCountFrequency (%)
0.4 1
< 0.1%
0.63 1
< 0.1%
0.68 1
< 0.1%
0.75 1
< 0.1%
0.79 1
< 0.1%
0.81 1
< 0.1%
0.82 1
< 0.1%
0.86 1
< 0.1%
0.87 1
< 0.1%
0.89 1
< 0.1%
ValueCountFrequency (%)
16870.98 1
< 0.1%
12307.16 1
< 0.1%
11140.56 1
< 0.1%
10734.57 1
< 0.1%
8145.52 1
< 0.1%
6330.91 1
< 0.1%
4906.33 1
< 0.1%
3995.45 1
< 0.1%
3768.66 1
< 0.1%
3278.11 1
< 0.1%

zipcode
Real number (ℝ)

Distinct60
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98117.006
Minimum98006
Maximum98272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:13.854639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98006
5-th percentile98101
Q198105
median98115
Q398122
95-th percentile98144
Maximum98272
Range266
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.677249
Coefficient of variation (CV)0.0001903569
Kurtosis10.429189
Mean98117.006
Median Absolute Deviation (MAD)10
Skewness1.9937018
Sum3.2623904 × 108
Variance348.83964
MonotonicityNot monotonic
2023-07-10T21:07:14.099564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98109 292
 
8.8%
98104 246
 
7.4%
98122 239
 
7.2%
98101 225
 
6.8%
98105 186
 
5.6%
98121 184
 
5.5%
98134 184
 
5.5%
98102 166
 
5.0%
98119 164
 
4.9%
98103 160
 
4.8%
Other values (50) 1279
38.5%
ValueCountFrequency (%)
98006 1
< 0.1%
98011 1
< 0.1%
98012 1
< 0.1%
98013 2
0.1%
98020 1
< 0.1%
98028 1
< 0.1%
98033 1
< 0.1%
98040 1
< 0.1%
98053 1
< 0.1%
98070 1
< 0.1%
ValueCountFrequency (%)
98272 1
 
< 0.1%
98204 1
 
< 0.1%
98199 70
2.1%
98198 1
 
< 0.1%
98195 10
 
0.3%
98191 1
 
< 0.1%
98185 1
 
< 0.1%
98181 1
 
< 0.1%
98178 4
 
0.1%
98177 2
 
0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2841
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.624081
Minimum47.49917
Maximum47.73387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:14.425931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.49917
5-th percentile47.541566
Q147.60001
median47.61875
Q347.65715
95-th percentile47.712964
Maximum47.73387
Range0.2347
Interquartile range (IQR)0.05714

Descriptive statistics

Standard deviation0.047774613
Coefficient of variation (CV)0.0010031608
Kurtosis-0.14284519
Mean47.624081
Median Absolute Deviation (MAD)0.02847
Skewness0.13624016
Sum158350.07
Variance0.0022824136
MonotonicityNot monotonic
2023-07-10T21:07:14.768697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.66246 9
 
0.3%
47.61598 7
 
0.2%
47.62208 6
 
0.2%
47.61543 5
 
0.2%
47.52549 5
 
0.2%
47.62395 5
 
0.2%
47.59938 4
 
0.1%
47.60071 4
 
0.1%
47.60427 4
 
0.1%
47.62014 4
 
0.1%
Other values (2831) 3272
98.4%
ValueCountFrequency (%)
47.49917 1
< 0.1%
47.50061895 1
< 0.1%
47.50224 1
< 0.1%
47.50959 1
< 0.1%
47.5097 1
< 0.1%
47.51018 1
< 0.1%
47.51042 1
< 0.1%
47.51098 1
< 0.1%
47.51104 1
< 0.1%
47.51127 2
0.1%
ValueCountFrequency (%)
47.73387 1
< 0.1%
47.73375 1
< 0.1%
47.73368 1
< 0.1%
47.7336 1
< 0.1%
47.73357 1
< 0.1%
47.73351 1
< 0.1%
47.73331 1
< 0.1%
47.73316 1
< 0.1%
47.73315 1
< 0.1%
47.73279 1
< 0.1%

longitude
Real number (ℝ)

Distinct2625
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.33476
Minimum-122.41425
Maximum-122.22097
Zeros0
Zeros (%)0.0%
Negative3325
Negative (%)100.0%
Memory size26.1 KiB
2023-07-10T21:07:14.985733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-122.41425
5-th percentile-122.38652
Q1-122.35056
median-122.33248
Q3-122.31947
95-th percentile-122.28971
Maximum-122.22097
Range0.1932841
Interquartile range (IQR)0.03109

Descriptive statistics

Standard deviation0.027177828
Coefficient of variation (CV)-0.0002221595
Kurtosis0.26702149
Mean-122.33476
Median Absolute Deviation (MAD)0.0151
Skewness-0.13538442
Sum-406763.06
Variance0.00073863435
MonotonicityNot monotonic
2023-07-10T21:07:15.197665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29898 8
 
0.2%
-122.35398 7
 
0.2%
-122.32468 6
 
0.2%
-122.33369 6
 
0.2%
-122.31769 5
 
0.2%
-122.32417 5
 
0.2%
-122.32592 5
 
0.2%
-122.33379 5
 
0.2%
-122.33064 5
 
0.2%
-122.32717 4
 
0.1%
Other values (2615) 3269
98.3%
ValueCountFrequency (%)
-122.41425 1
< 0.1%
-122.41182 1
< 0.1%
-122.41178 1
< 0.1%
-122.41169 1
< 0.1%
-122.41037 1
< 0.1%
-122.41036 1
< 0.1%
-122.41031 1
< 0.1%
-122.40976 1
< 0.1%
-122.40974 1
< 0.1%
-122.40901 1
< 0.1%
ValueCountFrequency (%)
-122.2209659 1
< 0.1%
-122.25864 1
< 0.1%
-122.26028 1
< 0.1%
-122.26034 1
< 0.1%
-122.26166 2
0.1%
-122.26172 1
< 0.1%
-122.26177 1
< 0.1%
-122.2618 1
< 0.1%
-122.26216 1
< 0.1%
-122.26223 1
< 0.1%

age
Real number (ℝ)

Distinct113
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.281203
Minimum8
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-07-10T21:07:15.412927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11
Q126
median48
Q375
95-th percentile115
Maximum123
Range115
Interquartile range (IQR)49

Descriptive statistics

Standard deviation33.023075
Coefficient of variation (CV)0.60837036
Kurtosis-0.86432259
Mean54.281203
Median Absolute Deviation (MAD)24
Skewness0.54349654
Sum180485
Variance1090.5235
MonotonicityNot monotonic
2023-07-10T21:07:15.618827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 71
 
2.1%
9 67
 
2.0%
34 64
 
1.9%
24 64
 
1.9%
15 64
 
1.9%
35 63
 
1.9%
55 63
 
1.9%
22 59
 
1.8%
21 58
 
1.7%
33 58
 
1.7%
Other values (103) 2694
81.0%
ValueCountFrequency (%)
8 35
1.1%
9 67
2.0%
10 50
1.5%
11 35
1.1%
12 15
 
0.5%
13 24
 
0.7%
14 41
1.2%
15 64
1.9%
16 42
1.3%
17 45
1.4%
ValueCountFrequency (%)
123 53
1.6%
122 8
 
0.2%
121 11
 
0.3%
120 3
 
0.1%
119 14
 
0.4%
118 9
 
0.3%
117 18
 
0.5%
116 31
0.9%
115 27
0.8%
114 31
0.9%

Interactions

2023-07-10T21:06:55.314845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:20.261035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:22.822597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:25.064008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:27.544841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:31.173852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:34.504718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:36.952953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:40.367033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:43.995465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:47.585119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:50.029881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:52.253269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:55.606056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:20.438116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:23.005338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:25.239764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:27.826665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:31.451990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:34.732680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:37.187259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:40.654208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:44.243766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:47.767642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:50.200060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:52.444963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:55.886324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:20.612208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:23.169889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:25.407000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:28.210996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:31.627109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:34.930683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:37.426389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:40.944824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:44.525852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:47.962485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:50.364240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:52.628430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:56.165287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:20.785963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:23.339493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:25.575793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:28.488392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:31.800364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:35.093978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:37.665403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:41.227614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:44.814198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:48.192261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:50.531651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:52.912754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:56.383188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:21.022845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:23.508571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:25.736059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:28.761477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:32.014163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:35.261974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:37.985861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:41.530685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:45.097730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:48.489835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:50.691644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:53.210908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:56.686723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:21.200449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:23.676462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:25.906927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:28.934217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:32.252580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:35.535987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:38.228124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:41.741451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:45.278689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:48.658759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:50.854406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:53.390182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:56.955256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:21.366535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:23.843021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:26.073716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:29.214616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:32.534323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:35.752073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:38.466319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:42.042067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:45.553978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:48.820889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:51.021231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:53.570042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:57.117686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:21.538708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:24.010381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:26.237890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:29.488979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:32.814859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:35.912625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:38.730659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:42.293077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:45.818344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:48.990386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:51.185298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:53.745616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:57.280575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:21.713445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:24.191012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:26.401888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:29.783856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:33.110794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:36.098096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:39.024426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:42.564831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:46.106622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:49.161220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:51.348001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:53.927815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:57.462320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:21.948841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:24.368974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:26.593762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:30.045804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:33.404122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:36.276592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:39.319280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:42.854810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:46.405121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:49.343193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:51.529517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:54.184355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:57.631567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:22.233245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:24.543835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:26.824579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:30.320721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:33.687814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:36.446326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:39.546297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:43.140520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:46.697362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:49.512505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:51.694286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:54.478343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:57.893823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:22.407840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:24.708925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:26.993637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:30.599710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:33.969375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:36.607865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:39.823339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:43.417275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:46.985931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:49.675966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:51.854842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:54.766624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:58.195677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:22.605092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:24.895194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:27.278250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:30.894099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:34.262683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:36.790549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:40.088242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:43.717417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:47.295841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:49.859845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:52.087608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-10T21:06:55.077968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-10T21:07:15.813378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
councildistrictcodenumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuilding_slargestpropertyusetypegfaenergystarscoretotalghgemissionszipcodelatitudelongitudeagebuildingtypeprimarypropertytypeneighborhoodsteamuseelectricitynaturalgasdefaultdata
councildistrictcode1.000-0.0380.3360.1550.1540.1440.1270.0750.123-0.1950.513-0.348-0.0010.1490.2530.8800.2140.0330.1390.098
numberofbuildings-0.0381.000-0.0430.102-0.0040.1040.1180.0280.0970.0380.0330.054-0.0460.2380.1530.0480.0810.0000.0000.000
numberoffloors0.336-0.0431.0000.4430.2620.4350.4160.1270.171-0.2290.066-0.114-0.2940.2450.2630.1370.2620.0000.0460.000
propertygfatotal0.1550.1020.4431.0000.3470.9830.9300.0830.584-0.091-0.057-0.022-0.3130.1440.1750.0570.1490.0000.0270.000
propertygfaparking0.154-0.0040.2620.3471.0000.2230.2740.0140.207-0.1240.015-0.053-0.2400.0520.1560.0610.0840.0000.0150.000
propertygfabuilding_s0.1440.1040.4350.9830.2231.0000.9280.0830.579-0.079-0.066-0.017-0.2850.1650.1910.0460.1250.0000.0250.000
largestpropertyusetypegfa0.1270.1180.4160.9300.2740.9281.0000.0930.570-0.053-0.049-0.013-0.2910.1480.2300.0450.1470.0000.0250.000
energystarscore0.0750.0280.1270.0830.0140.0830.0931.000-0.100-0.0030.085-0.037-0.0800.1190.1210.0570.0001.0000.1030.110
totalghgemissions0.1230.0970.1710.5840.2070.5790.570-0.1001.000-0.129-0.1100.026-0.0250.1260.2590.0000.1980.0000.0340.000
zipcode-0.1950.038-0.229-0.091-0.124-0.079-0.053-0.003-0.1291.000-0.0480.008-0.0880.0530.0760.2550.1500.0300.0880.064
latitude0.5130.0330.066-0.0570.015-0.066-0.0490.085-0.110-0.0481.000-0.027-0.1330.1510.2150.5880.3020.0360.1520.142
longitude-0.3480.054-0.114-0.022-0.053-0.017-0.013-0.0370.0260.008-0.0271.0000.0490.1270.1480.4900.1670.0630.0630.199
age-0.001-0.046-0.294-0.313-0.240-0.285-0.291-0.080-0.025-0.088-0.1330.0491.0000.1600.1870.1760.1550.0390.3420.046
buildingtype0.1490.2380.2450.1440.0520.1650.1480.1190.1260.0530.1510.1270.1601.0000.7320.2000.1950.1750.2830.708
primarypropertytype0.2530.1530.2630.1750.1560.1910.2300.1210.2590.0760.2150.1480.1870.7321.0000.2440.2950.1790.3490.606
neighborhood0.8800.0480.1370.0570.0610.0460.0450.0570.0000.2550.5880.4900.1760.2000.2441.0000.2880.0570.1580.149
steamuse0.2140.0810.2620.1490.0840.1250.1470.0000.1980.1500.3020.1670.1550.1950.2950.2881.0000.0000.0130.017
electricity0.0330.0000.0000.0000.0000.0000.0001.0000.0000.0300.0360.0630.0390.1750.1790.0570.0001.0000.0130.000
naturalgas0.1390.0000.0460.0270.0150.0250.0250.1030.0340.0880.1520.0630.3420.2830.3490.1580.0130.0131.0000.035
defaultdata0.0980.0000.0000.0000.0000.0000.0000.1100.0000.0640.1420.1990.0460.7080.6060.1490.0170.0000.0351.000

Missing values

2023-07-10T21:06:58.654341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-10T21:06:59.493981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-10T21:07:00.039863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

buildingtypeprimarypropertytypetaxparcelidentificationnumbercouncildistrictcodeneighborhoodnumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuilding_slistofallpropertyusetypeslargestpropertyusetypelargestpropertyusetypegfaenergystarscoresteamuseelectricitynaturalgasdefaultdatacompliancestatustotalghgemissionszipcodelatitudelongitudeage
0NonResidentialHotel06590000307DOWNTOWN11288434088434HotelHotel88434.060.0TrueTrueTrueFalseCompliant249.9898101.047.61220-122.3379996
1NonResidentialHotel06590002207DOWNTOWN1111035661506488502Hotel, Parking, RestaurantHotel83880.061.0FalseTrueTrueFalseCompliant295.8698101.047.61317-122.3339327
2NonResidentialHotel06590004757DOWNTOWN141956110196718759392HotelHotel756493.043.0TrueTrueTrueFalseCompliant2089.2898101.047.61393-122.3381054
3NonResidentialHotel06590006407DOWNTOWN11061320061320HotelHotel61320.056.0TrueTrueTrueFalseCompliant286.4398101.047.61412-122.3366497
4NonResidentialHotel06590009707DOWNTOWN11817558062000113580Hotel, Parking, Swimming PoolHotel123445.075.0FalseTrueTrueFalseCompliant505.0198121.047.61375-122.3404743
5Nonresidential COSOther06600005607DOWNTOWN12972883719860090Police StationPolice Station88830.0NaNFalseTrueTrueFalseCompliant301.8198101.047.61623-122.3365724
6NonResidentialHotel06600008257DOWNTOWN11183008083008HotelHotel81352.027.0FalseTrueTrueFalseCompliant176.1498101.047.61390-122.3328397
7NonResidentialOther06600009557DOWNTOWN181027610102761Other - Entertainment/Public AssemblyOther - Entertainment/Public Assembly102761.0NaNTrueTrueTrueFalseCompliant221.5198101.047.61327-122.3313697
8NonResidentialHotel09390000807DOWNTOWN1151639840163984HotelHotel163984.043.0FalseTrueTrueFalseCompliant392.1698104.047.60294-122.33263119
9Multifamily MR (5-9)Mid-Rise Multifamily09390001057DOWNTOWN1663712149662216Multifamily HousingMultifamily Housing56132.01.0TrueTrueTrueFalseCompliant151.1298104.047.60284-122.33184113
buildingtypeprimarypropertytypetaxparcelidentificationnumbercouncildistrictcodeneighborhoodnumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuilding_slistofallpropertyusetypeslargestpropertyusetypelargestpropertyusetypegfaenergystarscoresteamuseelectricitynaturalgasdefaultdatacompliancestatustotalghgemissionszipcodelatitudelongitudeage
3315Nonresidential COSOffice24250391377MAGNOLIA / QUEEN ANNE1113661013661OfficeOffice13661.075.0FalseTrueFalseTrueNaN3.5098119.247.63572-122.3752571
3316Nonresidential COSOther29250490873EAST1123445023445Other - RecreationOther - Recreation23445.0NaNFalseTrueTrueFalseCompliant259.2298106.047.63228-122.31574111
3317Nonresidential COSMixed Use Property75448002453CENTRAL1120050020050Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/ScienceOther - Recreation8108.0NaNFalseTrueTrueFalseCompliant60.8198126.447.60775-122.3022529
3318Nonresidential COSOffice41543005852SOUTHEAST1115398015398OfficeOffice15398.093.0FalseTrueTrueTrueNaN7.7998120.647.56440-122.2781363
3319Nonresidential COSOther25240390591DELRIDGE1118261018261Other - RecreationOther - Recreation18261.0NaNFalseTrueTrueFalseCompliant20.3398126.047.54067-122.3744141
3320Nonresidential COSOffice16240490802GREATER DUWAMISH1112294012294OfficeOffice12294.046.0FalseTrueTrueTrueNaN20.9498126.047.56722-122.3115433
3321Nonresidential COSOther35583000002DOWNTOWN1116000016000Other - RecreationOther - Recreation16000.0NaNFalseTrueTrueFalseCompliant32.1798116.047.59625-122.3228319
3322Nonresidential COSOther17945011507MAGNOLIA / QUEEN ANNE1113157013157Fitness Center/Health Club/Gym, Other - Recreation, Swimming PoolOther - Recreation7583.0NaNFalseTrueTrueFalseCompliant223.5498112.047.63644-122.3578449
3323Nonresidential COSMixed Use Property78836031551GREATER DUWAMISH1114101014101Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation6601.0NaNFalseTrueTrueFalseCompliant22.1198108.047.52832-122.3243134
3324Nonresidential COSMixed Use Property78570020302GREATER DUWAMISH1118258018258Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation8271.0NaNFalseTrueTrueFalseCompliant41.2798118.747.53939-122.2953685